Research Outputs

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    3D shape correspondence by isometry-driven greedy optimization
    (IEEE Computer Soc, 2010) N/A; Department of Computer Engineering; Sahillioğlu, Yusuf; Yemez, Yücel; PhD Student; Faculty Member; Department of Computer Engineering; Graduate School of Sciences and Engineering; College of Engineering; 215195; 107907
    We present an automatic method that establishes 3D correspondence between isometric shapes. Our goal is to find an optimal correspondence between two given (nearly) isometric shapes, that minimizes the amount of deviation from isometry. We cast the problem as a complete surface correspondence problem. Our method first divides the given shapes to be matched into surface patches of equal area and then seeks for a mapping between the patch centers which we refer to as base vertices. Hence the correspondence is established in a fast and robust manner at a relatively coarse level as imposed by the patch radius. We optimize the isometry cost in two steps. in the first step, the base vertices are transformed into spectral domain based on geodesic affinity, where the isometry errors are minimized in polynomial time by complete bipartite graph matching. the resulting correspondence serves as a good initialization for the second step of optimization in which we explicitly minimize the isometry cost via an iterative greedy algorithm in the original 3D Euclidean space. We demonstrate the performance of our method on various isometric (or nearly isometric) pairs of shapes for some of which the ground-truth correspondence is available.
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    A classification of concurrency bugs in java benchmarks by developer intent
    (Association for Computing Machinery (ACM), 2006) Department of Computer Engineering; Department of Computer Engineering; N/A; Keremoğlu, M. Erkan; Taşıran, Serdar; Elmas, Tayfun; Researcher; Faculty Member; PhD Student; Department of Computer Engineering; College of Engineering; College of Engineering; Graduate School of Sciences and Engineering; N/A; N/A; N/A
    This work addresses the issue of selecting the formal correctness criterion for a concurrent Java program that best corresponds to the developer's intent. We study a set of concurrency-related bugs detected in Java benchmarks reported in the literature. On these programs, we determine whether race-freedom, atomicity or refinement is the simplest and most appropriate criterion for program correctness. Our purpose is to demonstrate empirically the fact that the appropriate fix for a concurrency error and the selection of a program analysis tool for detecting such an error must be based on the proper expression of the designer's intent using a formal correctness criterion.
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    Adaptive peer-to-peer video streaming with optimized flexible multiple description coding
    (IEEE, 2006) Akyol, Emrah; Department of Electrical and Electronics Engineering; Department of Electrical and Electronics Engineering; Tekalp, Ahmet Murat; Civanlar, Mehmet Reha; Faculty Member; Faculty Member; Department of Electrical and Electronics Engineering; College of Engineering; College of Engineering; 26207; 16372
    Efficient peer-to-peer (P2P) video streaming is a challenging task due to time-varying nature of both the number of available peers and network/channel conditions. To this effect, we propose a receiver driven P2P streaming system which utilizes a flexible scalable multiple description coding method [1], where the number of base and enhancement descriptions, and the rate and redundancy level of each description can be adapted on the fly. The optimization of the parameters of the proposed MDC scheme according to network conditions is discussed within the context of the proposed adaptive P2P streaming framework, where the number and quality of available streaming peers/paths are a priori unknown and vary in time. Experimental results, by means of NS-2 network simulation of a P2P video streaming system, show that adaptation of the number, type, and rate of descriptions and the redundancy level of each description according to network conditions yields significantly superior performance when compared to MDC schemes using a fixed number of descriptions/layers with fixed rate and redundancy level.
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    Affective burst detection from speech using Kernel-fusion dilated convolutional neural networks
    (IEEE, 2022) N/A; N/A; Department of Computer Engineering; Köprü, Berkay; Erzin, Engin; N/A; Faculty Member; Department of Computer Engineering; N/A; College of Engineering; N/A; 34503
    As speech interfaces are getting richer and widespread, speech emotion recognition promises more attractive applications. In the continuous emotion recognition (CER) problem, tracking changes across affective states is an essential and desired capability. Although CER studies widely use correlation metrics in evaluations, these metrics do not always capture all the high-intensity changes in the affective domain. In this paper, we define a novel affective burst detection problem to capture high-intensity changes of the affective attributes accurately. We formulate a two-class classification approach to isolate affective burst regions over the affective state contour for this problem. The proposed classifier is a kernel-fusion dilated convolutional neural network (KFDCNN) architecture driven by speech spectral features to segment the affective attribute contour into idle and burst sections. Experimental evaluations are performed on the RECOLA and CreativeIT datasets. The proposed KFDCNN outperforms baseline feedforward neural networks on both datasets.
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    Application-layer qos fairness in wireless video scheduling
    (IEEE, 2006) N/A; N/A; Department of Electrical and Electronics Engineering; Department of Electrical and Electronics Engineering; Department of Electrical and Electronics Engineering; Özçelebi, Tanır; Sunay, Mehmet Oğuz; Tekalp, Ahmet Murat; Civanlar, Mehmet Reha; PhD Student; Faculty Member; Faculty Member; Faculty Member; Department of Electrical and Electronics Engineering; Graduate School of Sciences and Engineering; College of Engineering; College of Engineering; College of Engineering; College of Engineering; N/A; N/A; 26207; 16372
    In mobile video transmission systems, the initial delay for pre-fetching video at the client buffer needs to be short due to buffer limitations and application-layer user convenience. Therefore, an effective cross-layer wireless design is required that considers both physical and application layer aspects of such a system. We present a cross-layer optimized multi-user video adaptation and scheduling scheme for wireless video communication, where Quality-of-Service (QoS) fairness among users is provided while maximizing user convenience and video throughput. Application and physical layer aspects are jointly optimized using a Multi-Objective Optimization (MOO) framework that tries to schedule the user with the least remaining playback time and the highest video throughput (delivered video seconds per transmission slot) with maximum video quality. Experiments with the IS-856 (1xEV-DO) standard and ITU Pedestrian A and Vehicular B environments show the improvements over today's schedulers in terms of QoS fairness and user utility.
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    Detecting Javascript races that matter
    (Assoc Computing Machinery, 2015) Livshits, Benjamin; N/A; Department of Computer Engineering; Mutlu, Erdal; Taşıran, Serdar; PhD Student; Faculty Member; Department of Computer Engineering; Graduate School of Sciences and Engineering; College of Engineering; N/A; N/A
    As JavaScript has become virtually omnipresent as the language for programming large and complex web applications in the last several years, we have seen an increase in interest in finding data races in client-side JavaScript. While JavaScript execution is single-threaded, there is still enough potential for data races, created largely by the non-determinism of the scheduler. Recently, several academic efforts have explored both static and run-time analysis approaches in an effort to find data races. However, despite this, we have not seen these analysis techniques deployed in practice and we have only seen scarce evidence that developers find and fix bugs related to data races in JavaScript. In this paper we argue for a different formulation of what it means to have a data race in a JavaScript application and distinguish between benign and harmful races, affecting persistent browser or server state. We further argue that while benign races — the subject of the majority of prior work — do exist, harmful races are exceedingly rare in practice (19 harmful vs. 621 benign). Our results shed a new light on the issues of data race prevalence and importance. To find races, we also propose a novel lightweight run-time symbolic exploration algorithm for finding races in traces of run-time execution. Our algorithm eschews schedule exploration in favor of smaller run-time overheads and thus can be used by beta testers or in crowd-sourced testing. In our experiments on 26 sites, we demonstrate that benign races are considerably more common than harmful ones.
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    Dynamic haptic interaction with video
    (Crc Press-Taylor and Francis Group, 2015) N/A; Department of Electrical and Electronics Engineering; Department of Mechanical Engineering; Dindar, Nuray; Tekalp, Ahmet Murat; Başdoğan, Çağatay; N/A; Faculty Member; Faculty Member; Department of Electrical and Electronics Engineering; Department of Mechanical Engineering; Graduate School of Sciences and Engineering; College of Engineering; College of Engineering; N/A; 26207; 125489
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    Emotionally mediated spatial experience with AR
    (assoc Computing Machinery, 2017) N/A; Department of Media and Visual Arts; Department of Media and Visual Arts; Şemsioğlu, Sinem; Gökçe, Yağmur; Yantaç, Asım Evren; PhD Student; N/A; Faculty Member; Department of Media and Visual Arts; N/A; College of Social Sciences and Humanities; College of Social Sciences and Humanities; N/A; N/A; 52621
    This paper speculates and explores how emotional awareness and communication can be enhanced with the mediation of spatial experience. Based on two exploratory user studies, we designed and prototyped a conceptual system that mediates the spatial attributes of the surroundings according to user's choices and their emotional state. We then conducted user studies with the prototype. We contribute to existing literature by sharing our insights into potential use cases and implications of an emotionally responsive space.
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    PublicationOpen Access
    Federated dropout learning for hybrid beamforming with spatial path index modulation in multi-user MMWave-MIMO systems
    (Institute of Electrical and Electronics Engineers (IEEE), 2021) Mishra, Kumar Vijay; Department of Electrical and Electronics Engineering; Ergen, Sinem Çöleri; Elbir, Ahmet Musab; Faculty Member; Department of Electrical and Electronics Engineering; College of Engineering; 7211; N/A
    Millimeter wave multiple-input multiple-output (mmWave-MIMO) systems with small number of radio-frequency (RF) chains have limited multiplexing gain. Spatial path index modulation (SPIM) is helpful in improving this gain by utilizing additional signal bits modulated by the indices of spatial paths. In this paper, we introduce model-based and model-free frameworks for beamformer design in multi-user SPIM-MIMO systems. We first design the beamformers via model-based manifold optimization algorithm. Then, we leverage federated learning (FL) with dropout learning (DL) to train a learning model on the local dataset of users, who estimate the beamformers by feeding the model with their channel data. The DL randomly selects different set of model parameters during training, thereby further reducing the transmission overhead compared to conventional FL. Numerical experiments show that the proposed framework exhibits higher spectral efficiency than the state-of-the-art SPIM-MIMO methods and mmWave-MIMO, which relies on the strongest propagation path. Furthermore, the proposed FL approach provides at least 10 times lower transmission overhead than the centralized learning techniques.
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    PublicationOpen Access
    Generalized Polytopic Matrix Factorization
    (Institute of Electrical and Electronics Engineers (IEEE), 2021) Department of Electrical and Electronics Engineering; Erdoğan, Alper Tunga; Tatlı, Gökcan; Faculty Member; Department of Electrical and Electronics Engineering; College of Engineering; Graduate School of Sciences and Engineering; 41624; N/A
    Polytopic Matrix Factorization (PMF) is introduced as a flexible data decomposition tool with potential applications in unsupervised learning. PMF assumes a generative model where observations are lossless linear mixtures of some samples drawn from a particular polytope. Assuming that these samples are sufficiently scattered inside the polytope, a determinant maximization based criterion is used to obtain latent polytopic factors from the corresponding observations. This article aims to characterize all eligible polytopic sets that are suitable for the PMF framework. In particular, we show that any polytope whose set of vertices have only permutation and/or sign invariances qualifies for PMF framework. Such a rich set of possibilities enables elastic modeling of independent/dependent latent factors with combination of features such as relatively sparse/antisparse subvectors, mixture of signed/nonnegative components with optionally prescribed domains.